Zusammenfassung
The growing popularity of black box machine learning methods for medical image analysis makes their interpretability to a crucial task. To make a system, e.g. a trained neural network, trustworthy for a clinician, it needs to be able to explain its decisions and predictions. In our work we tackle the problem of explaining the predictions of medical image classifiers, trained to differentiate between different types of pathologies and healthy tissue [1].
Chapter PDF
Literatur
Uzunova H, Ehrhardt J, Kepp T, et al. Interpretable explanations of black box classifiers applied on medical images by meaningful perturbations using variational autoencoders. Proc SPIE. 2019;Accepted.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Fachmedien Wiesbaden GmbH, ein Teil von Springer Nature
About this paper
Cite this paper
Uzunova, H., Ehrhardt, J., Kepp, T., Handels, H. (2019). Abstract: Interpretable Explanations of Black Box Classifiers Applied on Medical Images by Meaningful Perturbations Using Variational Autoencoders. In: Handels, H., Deserno, T., Maier, A., Maier-Hein, K., Palm, C., Tolxdorff, T. (eds) Bildverarbeitung für die Medizin 2019. Informatik aktuell. Springer Vieweg, Wiesbaden. https://doi.org/10.1007/978-3-658-25326-4_42
Download citation
DOI: https://doi.org/10.1007/978-3-658-25326-4_42
Published:
Publisher Name: Springer Vieweg, Wiesbaden
Print ISBN: 978-3-658-25325-7
Online ISBN: 978-3-658-25326-4
eBook Packages: Computer Science and Engineering (German Language)